Abstract. Telehealth has long been touted as an efficient and cost-effective way to deliver health care services to patients. The COVID-19 global pandemic hastened the adoption of this technology in the United States. Despite its promises, telehealth as a technology-based model of health service delivery has also highlighted access to care inequities in the form of uneven utilization across various patient demographics. This research uses machine learning and publicly available data sources to describe telehealth utilization based on social determinants of care. The implications of this application can be used to inform health care providers of how to target efforts to improve access to care for their patient populations.
Chau, Quynh; Behuria Pathak, Amita; Turner, Daniel; Cheun, Jacquelyn; and Noe, Carl
"Access Barriers To Telehealth,"
SMU Data Science Review: Vol. 5
, Article 8.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss3/8
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